In electric and hybrid electric vehicles (HEVs), long and short distance travel or trips can be unnecessarily costly, and can be extended and/or delayed by the need for recharging of batteries. With the gradual increase in high power and more efficient road side charge stations, such travel time and costs may be reduced, so long as HEVs can be optimally routed to the most efficient, least expensive, and most readily available road side charging stations. Charge performance of HEVs and charge stations is affected by the ambient environment, vehicle performance, charging station capabilities and efficiency, availability due to use and maintenance issues, cost of electricity, and other factors, which can introduce undesirable errors in travel route planning and charge time estimates across such planned routes. In view of the stochastic nature of ever changing charge station locations, efficiency, availability, and costs, such travel planning and charge time estimate errors have persisted despite some attempts to improve accuracy.
Some such attempts have been directed to predicting HEV range of operation estimates, predicting optimal charge station locations, and/or predicting real-time HEV performance. Such attempts appear to have utilized known HEV and charge station performance and look-up table algorithms, which seem to have been employed in different ways with controllers located on board the HEVs. With the vastly increasing number of variables and parameters that define the continuously changing performance of HEVs across a global fleet, and the location and efficiency of charge stations, the capability to accurately estimate optimized routes to high power charge stations has substantially exceeded the technological capability of such on-board HEV processors.